One-Class Genetic Algorithm for Authentication Analysis of Spectrochemical Data.

ABSTRACT: One-class classifier (OCC) models are widely applied to solve classification problems where control or class modeling from a target class is necessary. In this study, OCC models such as Data Driven Soft Independent Modeling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) were associated with a new variable selection strategy, the one-class genetic algorithm (OGA), for classification analyses in three clinical applications: COVID-19, endometriosis, and dengue samples. DD-SIMCA was implemented in a rigorous approach, using α = 0.05, while OCPLS was performed with partial robust M-regression (PRM). For the three cases, a better classification performance was obtained using the OGA associated with the OCC model. The performance of the OGA-PRM-OCPLS showed better results

SUBMITTER: de Morais Filho JR 

PROVIDER: S-EPMC12824936 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature